Eye-tracking has emerged as a popular method for empirical studies of cognitive processes across multiple substantive research areas. Eye-tracking systems are capable of automatically generating fixation-location data over time at high temporal resolution. Often, the researcher obtains a binary measure of whether or not, at each point in time, the participant is fixating on a critical interest area or object in the real world or in a computerized display. Eye-tracking data are characterized by spatial-temporal correlations and random variability, driven by multiple fine-grained observations taken over small time intervals (e.g., every 10 ms). Ignoring these data complexities leads to biased inferences for the covariates of interest such as experimental condition effects. This article presents a novel application of a generalized additive logistic regression model for intensive binary time series eye-tracking data from a between- and within-subjects experimental design. The model is formulated as a generalized additive mixed model (GAMM) and implemented in the mgcv R package. The generalized additive logistic regression model was illustrated using an empirical data set aimed at understanding the accommodation of regional accents in spoken language processing. Accuracy of parameter estimates and the importance of modeling the spatial-temporal correlations in detecting the experimental condition effects were shown in conditions similar to our empirical data set via a simulation study.

中文翻译：

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使用广义加性逻辑回归模型对密集二进制时间序列眼动跟踪数据进行时空建模。

眼动追踪已成为跨多个实质性研究领域的认知过程实证研究的流行方法。眼动追踪系统能够随时间以高时间分辨率自动生成注视位置数据。通常，研究人员会获得一个二进制衡量标准，即参与者是否在每个时间点都注视着现实世界或计算机显示器中的关键兴趣区域或对象。眼动追踪数据的特点是时空相关性和随机变异性，由在小时间间隔（例如，每 10 毫秒）内进行的多个细粒度观察驱动。忽略这些数据的复杂性会导致对感兴趣的协变量（例如实验条件效应）的有偏见的推断。本文介绍了广义加性逻辑回归模型的一种新应用，该模型用于来自受试者间和受试者内实验设计的密集二元时间序列眼动追踪数据。该模型被制定为广义加性混合模型 (GAMM)，并在 mgcv R 包中实现。使用旨在了解口语处理中区域口音适应的经验数据集说明了广义加性逻辑回归模型。通过模拟研究，在与我们的经验数据集相似的条件下，显示了参数估计的准确性和在检测实验条件效应中建模时空相关性的重要性。该模型被制定为广义加性混合模型 (GAMM)，并在 mgcv R 包中实现。使用旨在了解口语处理中区域口音适应的经验数据集说明了广义加性逻辑回归模型。通过模拟研究，在与我们的经验数据集相似的条件下，显示了参数估计的准确性和在检测实验条件效应中建模时空相关性的重要性。该模型被制定为广义加性混合模型 (GAMM)，并在 mgcv R 包中实现。使用旨在了解口语处理中区域口音适应的经验数据集说明了广义加性逻辑回归模型。通过模拟研究，在与我们的经验数据集相似的条件下，显示了参数估计的准确性和在检测实验条件效应中建模时空相关性的重要性。